Sustainability Indicator Selection by a Novel Triangular Intuitionistic Fuzzy Decision-Making Approach in Highway Construction Projects

The construction industry has been criticized as being a non-sustainable industry that requires effective tools to monitor and improve its sustainability performance. The multiplicity of indicators of the three pillars of sustainability—economic, social, and environmental—complicates construction sustainability assessments for project managers. Therefore, prioritizing and selecting appropriate sustainability indicators (SIs) is essential prior to conducting a construction sustainability assessment. The main purpose of this research is to select the most appropriate set of SIs to address all three pillars of highway sustainability by a new group decision-making approach. The proposed approach accounts for risk attitudes of experts and entropy measures under a triangular intuitionistic fuzzy (TIF) environment, to handle the inherent uncertainty and vagueness that is present throughout the evaluation process. Furthermore, new separation measures and ranking scores are introduced to distinguish the preference order of SIs. Eventually, the approach is implemented in a case study of highway construction projects and the applicability of the approach is examined. To investigate the stability and validity of computational results, a sensitivity analysis is carried out and a comparison is made between the obtained ranking outcomes and the traditional decision-making methods.


Introduction
The preliminary concept of sustainable development was introduced in the 1980s [1]. According to the World Commission on Environment and Development (WCED) report, sustainable development refers to development that can be useful for nature, not harmful and aids in meeting the needs of present generations without compromising the needs of future generations. Sustainable development is generally balanced among three aspects or pillars: economic, environmental and social sustainability and aims to meet all these needs/objectives simultaneously [2].
In recent decades, sustainable construction-as a fundamental contributor towards sustainable development-has been the focus of a great deal of research. Recent research efforts have largely concentrated on the performance measurement process and sustainability assessment in building and construction projects, based on analytical and computational evaluation approaches, sustainable construction tools, standards and rating systems, or a combination of these. Indeed, these studies have attempted-by various techniques-to aid the construction industry in reaching sustainable development ideals and goals. As illustrated below, some of these research studies have been reviewed. Yu et al. [3] provided the project management team with planning strategies using a sustainability-assessing system. The proposed system was developed to monitor and evaluate the sustainability of whole activities throughout the construction projects' life cycle. Goubran and Cucuzzella [4] presented two analytical mapping tools for design teams of building projects to utilize Sustainable Development Goals (SDGs) as a sustainability analyzing framework in the design process. The first tool was constructed based on distinguishing between the architectural, engineering and operational concerns, while the second tool was designed based on the characteristics of the design approach (either product or human-focused) and its inspiration (history vs. future driven). Karaca et al. [5] developed a rapid sustainability assessment method using indicators and their relative weights attained from stakeholders, and an assessment approach based on the responses of buildings' occupants to measure the sustainability performance of residential buildings in Nur-Sultan, Kazakhstan. Li et al. [6] provided a comprehensive analysis of various stakeholder groups associated with sustainable construction in China. In addition, the level of stakeholder influence in decision/evaluations was measured using semi-structured interviews and the Delphi technique. Omer and Noguchi [7] developed a conceptual framework for the selection of appropriate building materials considering the implementation of the 2030 Agenda for Sustainable Development. Indeed, they presented a knowledge-based decision support system to assist policymakers, designers and construction stakeholders in making appropriate decisions towards the achievement of SDGs. Xu et al. [8] evaluated the sustainability of the construction industry by an assessment model based on the entropy method in China. The level of sustainability in construction projects was determined by two indices named the social, economic, and environmental benefits index and the ecological costs index. Illankoon et al. [9] suggested a scoring model regarding the inter-links between the Leadership in Energy and Environmental Design (LEED) credits and SDGs to evaluate buildings constructed in Australia. Their proposed model identified a Comprehensive Contribution to Development Index (CCDI) to policymakers as a guideline for evaluating building projects in order to achieve the United Nations (UN)'s SDGs. Olawumi et al. [10] introduced a grading system of buildings in Nigeria, named the Building Sustainability Assessment Method (BSAM) scheme. The scheme involves the identification of key sustainability assessment criteria and assigns weighted-scores to the various criteria by the multi-expert consultation method. Mansell et al. [11] used empirical evidence to identify a golden thread between sustainability reporting frameworks at the project level and the organizational level, and impacts of the UN's SDGs. The frameworks benefit from the Ceequal reporting methodology at the project level and the Global Reporting Initiative (GRI) methodology at the organizational level. Accordingly, a database of indicators was extracted that aligned with the specific SDG targets. Additionally, a robust investment appraisal was provided for the design stage of infrastructure projects.
Since highway projects are one of the most important aspects for the development of transportation infrastructure-necessary due to higher population concentrations and greater transportation demands in urban areas [12,13]-they have been considered as one of the most crucial components of sustainable development. Moreover, highway construction projects use a vast quantity of energy and natural materials, generate waste, and produce greenhouse gases that can greatly affect the sustainability of the construction industry.
The sustainability indicators (SIs) are significant factors in the sustainability assessment of highway projects. Various studies and tools have introduced numerous SIs for the assessment of the sustainability of construction projects that has led to the complication of the assessment process. Therefore, the prioritization of indicators and the adoption of an optimal number of SIs are major issues. In addition, the evaluation of SIs is complex for decision-makers, owing to inadequate evidence and uncertainty surrounding highway construction projects [13][14][15]. Hence, Multi-Criteria Decision-Making (MCDM) techniques under uncertainty are useful tools to cope with these problems [16][17][18][19][20]. While the construction industry plays an important role in global sustainable development, numerous research efforts have studied different subjects regarding the sustainability of the construction industry and the use of multi-criteria decision making to evaluate sustainability in this industry. Huang and Yeh [21] developed a framework to analyze the green highway projects applying the max-min fuzzy Delphi method to recognize the main classifications and related items. Chen et al. [22] proposed a model called the construction method selection model aimed at lending support to assess the prefabrication feasibility at the initial level utilizing the simple multi-attribute rating technique and subsequently to adopt the best strategy to employ prefabrication at the following level. Reza et al. [23] proposed a thorough assessment technique using Triple Bottom Line (TBL) criteria to assess flooring systems with respect to the combination of Analytic Hierarchy Process (AHP) and Life Cycle Analysis (LCA) techniques. Waris et al. [24] established criteria for selecting sustainable construction equipment based on qualitative and quantitative feedbacks of construction industry experts and finally selected the top five criteria. Li et al. [25] proposed a comprehensive methodology using entropy, which is suitable for calculating weights, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods at the same time to evaluate the development of highway transportation. Kucukvar et al. [26] presented a fuzzy MCDM method for prioritizing pavements and selecting the best one based on the respective sustainability performance using the TOPSIS method. Medineckiene et al. [27] proposed a novel MCDM technique to adopt criteria from which their sets and weights are determined in accordance with the Swedish certification system Miljöbyggnad and used AHP for building sustainability assessment. Kamali and Hewage [28] identified sustainability performance indicators to evaluate life cycle sustainability. Subsequently, an organized framework was developed based on designing and conducting a survey to choose the most suitable sustainability performance indicators for modular and conventional construction methods in North America. Pan et al. [29] developed a sustainability indicator framework to reliably assess the performance of construction automation and robotics in the building industry context. Indeed, the study proposed guidelines for sustainable automated and robotic options for advanced construction technology. Zolfani et al. [30] presented a hybrid MCDM methodology applying Step-wise Weight Assessment Ratio Analysis (SWARA) and Complex Proportional Assessment (COPRAS) for criteria weights and prioritizing alternatives, respectively.
Liu and Qian [31] developed an integrated sustainability assessment methodology in accordance with the life cycle sustainability assessment framework. In addition, a combination of AHP and ELimination Et Choice Translating REality (ELECTRE) was applied to derive criteria weights and prioritize alternatives. Reddy et al. [32] introduced a decisionmaking method to adopt a sustainable material without Life Cycle Inventory (LCI) information requirements. In this method, criteria that highly influence material sustainability were investigated and consequently applied to analyze the performance of materials in different aspects of the material life cycle to develop a sustainable material performance index utilizing AHP. Chen [33] used a new multi-criteria assessment approach integrating the Grey Relational Analysis (GRA) and TOPSIS techniques, which operate according to the intuitionistic fuzzy entropy method, for selection of the appropriate sustainable supplier of construction materials. Roy et al. [34] developed a combinative distance-based evaluation method utilizing Interval-Valued Intuitionistic Fuzzy Numbers (IVIFNs) to decide comprehensively and logically to deal with the problem of material adoption under uncertainty. Tseng et al. [35] introduced various features and measures to build a model and assess the construction projects in Ecuador, employing fuzzy decision-making trials with Decision Making Trial and Evaluation Laboratory (DEMATEL) in addition to an Analytic Network Process (ANP) to evaluate interdependence between the features of a sustainable product-service system. Hendiani and Bagherpour [14] presented a novel social sustainability performance assessment in construction projects using fuzzy numbers to evaluate the present social sustainability position associated with construction. Furthermore, the barriers that reduce the value of the social sustainability index were recognized and addressed. Alawneh et al. [36] proposed a novel framework that identifies and weighs SIs for sustainable non-residential buildings and contributes to achieving the SDGs in Jordan. The framework applies the Delphi technique to identify and categorize SIs and then integrates AHP and Relative Importance Index (RII) methods to weigh SIs. In addition, a management tool (Gantt chart) integrates SIs into the project phases towards sustainable construction management. Dabous et al. [37] proposed a multi-criteria decision-support approach to handle decision-making in sustainable pavement adoption. The main sustainable decision factors were recognized through a hierarchy structure in their approach. In addition, the AHP technique in combination with multi-attribute utility theory was used to rank the networks of pavement sections. For sustainable landfill site selection, Rahimi et al. [38] introduced a Geographical Information System (GIS)-MCDM methodology considering the group fuzzy Best-Worst Method (BWM), fuzzy MULTIMOORA method and GIS-based suitability maps. The methodology was employed in Mahallat city, Iran, and it could provide suitable guidance for the waste management department of municipalities. Navarro et al. [39] developed an assessment methodology to measure the sustainability performance of the concrete bridge deck based on a neutrosophic group AHP approach. In addition, the TOPSIS technique was utilized to aggregate the sustainability criteria.
The aforementioned studies demonstrate that the previous research did not pay much attention to the sustainability performance of highway construction projects. Furthermore, there are no studies focusing on prioritizing and selecting the indicators of the three sustainability pillars. For the recognized gaps, a new multi-criteria weighting and ranking approach, according to group decision-making, is presented in the present study to analyze and adopt SIs in highway construction projects. Initially, SIs and criteria are collected and listed concerning experts' views and the literature review. Thus, triangular intuitionistic fuzzy (TIF) decision matrices are constructed based on experts' views in terms of linguistic variables. Subsequently, the weights of experts are gained according to the concept of entropy. Afterward, primary weight vectors of criteria are specified by entropy measures and experts' views. Finally, SIs are ranked based on the positive and negative ideal separation matrices via presenting a new ranking score. Moreover, a case study in highway construction projects is addressed to demonstrate the efficiency of the presented approach.
The rest of this paper is organized as follows. In Section 2, a novel multi-criteria group decision-making approach is proposed and applied in a case study of a highway construction project. Section 3 presents the results of the approach implementation in detail. The obtained results are compared with the prevalent decision-making methods and other mentioned SIs in the cited literature, and the sensitivity analyses are conducted in Section 4. To conclude the paper, Section 5 depicts the concluding remarks.

TIF Group Decision-Making Approach
The proposed approach aims to assist project managers in selecting the most significant SIs for sustainability assessment of highway construction projects based on a novel TIF group decision-making approach. Figure 1 presents the proposed approach for the selection of SIs.
The phases of the presented approach are as follows: Step 1. Constitute a group of experts (E e ; e = 1, 2, . . . , t), whose views and judgments will be employed to build and assess the problem.
Step 2. Gather a list of indicators that are possible to be applied for the sustainability evaluation of highway construction projects (I i ; i = 1, 2, . . . , m).
Step 4. Assign the risk attitude to each expert and incorporate it into the related triangular intuitionistic fuzzy numbers (TIFNs) (Definition A1). In Table 1, indicates the hesitation degree of TIFN ⟨( , , ); , ⟩ and is equal to 1 − − Step 5. Construct the primary decision matrices based on the experts' views.
The primary decision matrices are constructed from the performance rating of each indic versus each criterion based on the experts' view in terms of linguistic terms ( Table 2) and conve into TIFNs.    Each expert is assigned a risk attitude according to his or her character. The risk attitudes are able to be specified by a higher management level and expressed by linguistic variables, like absolutely optimistic (AO), optimistic (O), neutral (N), pessimistic (P), and absolutely pessimistic (AP) [40,41], for 5-scale TIFNs (Table 1). Table 1. Linguistic variables of the risk attitudes assigned to each expert for 5-scale triangular intuitionistic fuzzy numbers (TIFNs).

Linguistic Variables
TIFN Derived from 〈(a,b, c);µ, ν〉 for Benefit Criteria TIFN Derived from 〈(a,b, c);µ, ν〉 for Cost Criteria In Table 1, π indicates the hesitation degree of TIFN (a, b, c) ; µ, ν and is equal to Step 5. Construct the primary decision matrices based on the experts' views.
The primary decision matrices are constructed from the performance rating of each indicator versus each criterion based on the experts' view in terms of linguistic terms ( Table 2) and converted into TIFNs.
The primary decision matrices are converted to decision matrices taking into account each expert's risk attitude according to Table 1.
Step 7. Compute each expert's entropy-weight according to the individual decision matrices.
Step 8. Build the aggregated TIF decision matrix taking into account the entropy-weights of experts.
According to the TIF weighted geometric aggregation (TIFWGA) operator [44], the aggregated TIF decision matrix concerning the entropy-weights of experts is gained as follows: Step 9. Construct the primary weight vectors of criteria based on experts' views.
The significance of criteria is provided based on the experts' views in terms of linguistic terms (Table 3). Step 10. Convert the primary weight vectors to the individual weight vectors based on each expert's risk attitude.
The primary weight vectors are converted to the individual weight vectors taking into account each expert's risk attitude according to Table 1.
Step 11. Compute each expert's entropy-weight according to the weight vectors.
The entropy measure G e j is calculated by [42]: where Thus, each expert's entropy-weight according to the expert-based weight vector is determined as follows [42,43]: where 0 ≤ β e j ≤ 1, and ∑ t e=1 β e j = 1.
Step 12. Provide the TIF weight vector of the criteria.
The TIF weight vector W is built according to experts' entropy-weight by using Definition A2 as follows: where Step 13. Compute the TIF positive-ideal solution (PIS) and the TIF negative-ideal solution (NIS) vectors.
The TIF PIS r * j and the TIF NIS r − j are, respectively, defined as follows: Sustainability 2020, 13, 1477 where J 1 and J 2 are the benefit criteria and cost criteria, respectively.
The PISE matrix (∆ * ) and the NISE matrix (∆ − ) are defined based on hamming distance [45]. and Step Step 16. Calculate the κ i and ϑ i values.
The values of indices κ i and ϑ i are calculated as follows: and Sustainability 2020, 13, 1477 ψ are regarded as the relative importance for the strategy of the majority attributes, whereas 1 − χ and 1 − ψ are the relative importance of the individual regret.
Step 17. Compute the novel ranking score.
The ranking scores C i are defined as follows: Step 18. Rank the SIs according to the ranking score (C i values).
The SIs are sorted by the C i values in decreasing order. The maximum value of the C i indicates the higher importance.

Case Study
The efficiency of the presented approach was examined through a case study of a highway construction project. To that end, an Iranian construction firm was involved in transportation infrastructures. The firm has numerous highway construction projects being built in various areas of the country. To evaluate the projects according to sustainable construction principles, the firm managers intended to recognize and prioritize SIs to adopt the key evaluation indicators from a pool of numerous SIs in these projects.
where C t is the net cash inflow during the period t, C 0 is the total initial investment cost and t is the number of time periods

Results
The primary decision matrices are constructed by experts employing linguistic terms in Table 2. The matrices are shown in Table 7 (Step 5). Afterward, the primary decision matrices are converted to decision matrices taking into account each expert's risk attitude according to Table 1 (Table 8) (Step 6). Owing to space limitations, only the outcomes associated with three SIs (SoI 6 , EcI 4 and EnI 10 ) are shown as a sample of each dimension of sustainability in some of the following tables.
Then, each expert's entropy-weight and aggregated TIF decision matrix is calculated. The outcomes of these steps are represented in Tables 9 and 10, respectively (Steps 7 and 8). In addition, the criteria weight vector is achieved according to experts' preferences and is illustrated in Table 11 (Step 9). According to steps 10 to 12, based on criteria weight vector, expert's risk attitude and expert's entropy-weight, the TIF criteria weight vectors are built. The results are presented in Table 12. Next, the TIF PIS and NIS vectors are specified as given in Table 13 (Step 13). Then, as shown in Table 14, the PISE matrix (D * ) and NISE matrix (D − ) are built (Step 14).         Table 10. Aggregated triangular intuitionistic fuzzy (TIF) decision matrix ( R).  Table 11. Criteria weight vector based on experts' preferences. I  I  I  I  M  M  I  E 2  M  VI  I  VI  I  UI  I  E 3  I  I  I  I  I  M  I  E 4  M  I  VI  I  M  M  VI  E 5  I  VI  I  I  I  M  VI   Table 12. TIF and crisp criteria weight vectors.  Table 14. Positive-ideal separation (PISE) and negative-ideal separation (NISE) matrices.

Ideal Separation SIs
Criteria Ultimately, the A i , B i , A i , B i , κ i and ϑ i values are calculated (χ and ψ are considered 0.5). Then, the novel ranking score is calculated (η considered 0.5), and SIs are prioritized according to ranking score (C i values). The gathered results are presented in Table 15 (Step 15 to 18). Table 15. Computational results of the proposed approach and final ranking of SIs.  Table 15. Computational results of the proposed approach and final ranking of SIs.

Discussion
The results achieved from the presented approach are examined comprehensively, and the accuracy, precision, and sensitivity of the answers are investigated in this section. A thorough sensitivity analysis is performed on ten SIs with a higher priority for various values of approach variables. Furthermore, the comparisons are made between the outcomes of the presented approach and other cited literature.

Sensitivity Analysis
A comprehensive sensitivity analysis is conducted in this subsection. First, the sensitivity of ranking score (C values) and ranking orders are investigated for values of χ, ψ and η ranging from 0 to 1 (Figures 2 and 3).
In Figure 2a,b, the C values and ranking orders are represented for various values of χ and ψ ranging from 0 to 1, respectively. As represented in Figure 2a, the graph of the C values for various SIs versus χ and ψ values ranging from 0 to 1 has three states of almost constant, ascending or descending. However, in most cases, the graphs of indicators are parallel, and only a few intersections are represented for χ and ψ values above 0.7.
As can be seen in Figure 2b, changing the graph for χ and ψ values above 0.7 leads to few changes in the ranking order of SIs. In addition, for variations of χ and ψ between 0.2 and 0.7, the rank of all SIs remains unchanged, and also a set of the top ten SIs remains in a range from 1 to 10. Hence, the conclusion can be drawn that the top ten SIs have the lowest sensitivity to the values of χ and ψ between 0.2 and 0.7, and the assumed value of 0.5 for this variable in the case study is suitable. are represented for and values above 0.7. As can be seen in Figure 2b, changing the graph for and values above 0.7 leads to few changes in the ranking order of SIs. In addition, for variations of and between 0.2 and 0.7, the rank of all SIs remains unchanged, and also a set of the top ten SIs remains in a range from 1 to 10. Hence, the conclusion can be drawn that the top ten SIs have the lowest sensitivity to the values of and between 0.2 and 0.7, and the assumed value of 0.5 for this variable in the case study is suitable.  are represented for and values above 0.7. As can be seen in Figure 2b, changing the graph for and values above 0.7 leads to few changes in the ranking order of SIs. In addition, for variations of and between 0.2 and 0.7, the rank of all SIs remains unchanged, and also a set of the top ten SIs remains in a range from 1 to 10. Hence, the conclusion can be drawn that the top ten SIs have the lowest sensitivity to the values of and between 0.2 and 0.7, and the assumed value of 0.5 for this variable in the case study is suitable.
(a) (b)  In Figure 3a,b, C values and ranking orders versus η values ranging from 0 to 1 are represented, respectively. Figure 3a represents that the graph of C values for all SIs is ascending by increasing the η value from 0 to 1. However, according to the figure, the gap between the values of C increases by decreasing η. Thus, for smaller values of η, the gap between the C values of different SIs is larger, allowing an accurate distinction for decisionmakers. In addition, according to Figure 3b, it can be concluded that the ranking order of all top SIs is constant for η values other than EnI 2 for the value of η = 0.2.
With these in mind, this conclusion can be drawn that C values and ranking orders have no sensitivity to η. Hence, choosing 0.5 as a median number of the interval for η in the case study is a suitable choice.

Comparison between the Proposed Approach and Other Cited Literature
To validate the presented approach outcomes, a comparison is made between the achieved results by the proposed approach and the traditional fuzzy MCDM methods. Table 16 shows the outcomes of this comparison. The comparison results for the top ten SIs are also represented in Figure 4.  As presented in Table 16 and Figure 4, the priority of SIs derived from the presented approach is not very different from other methods in most cases (in most cases, the number of ranks is changed by up to three or four rank shifts in SIs priority). Besides, EcI4 has the first priority in all methods, and EnI5 has the last priority in the presented approach and all methods. For the ten first priorities As presented in Table 16 and Figure 4, the priority of SIs derived from the presented approach is not very different from other methods in most cases (in most cases, the number of ranks is changed by up to three or four rank shifts in SIs priority). Besides, EcI 4 has the first priority in all methods, and EnI 5 has the last priority in the presented approach and all methods. For the ten first priorities that are key indicators, despite some changes of ranks (at most four ranks) in some methods, the priorities of indicators remain within the ten first priorities except EnI 2 . Furthermore, as can be observed in the figure, most key indicators have the same ranks in the proposed approach and all traditional methods. However, SoI 4 had relatively more changes, which can be considered as the most sensitive key indicator.
From the above, the conclusion can be drawn that the proposed approach is reliable and its results benefit from the merits of taking into account risk attitudes of experts, concepts of entropy in determining weights of experts, and a new TIFS-ranking approach concurrently.
As presented in Table 17, most of the ten first priorities have been utilized as SIs' assessment in the cited literature. Much higher adaptation is related to Yao et al. [59] and Envision [64] and less adaptation is related to Awasthi et al. [47]. Three SIs of ten key indicators, SoI 1 , SoI 4 and EnI 2 , exist in all cited literature. In addition, EnI 10 is introduced in all the literature except Awasthi et al. [47]. In addition, it can be observed that the social and environmental indicators have been incorporated in all cited tools (except SoI 6 in Invest) but economic indicators have not been considered in the cited tools (except EcI 4 ). These comparisons demonstrate that the outcomes of the approach are reliable and can be employed in a sustainable assessment of highway construction projects.

Concluding Remarks
Analyzing sustainability indicators (SIs) in construction projects between different potential indicators and considering various assessment criteria concurrently can be considered as a complicated group decision problem. A new triangular intuitionistic fuzzy set (TIFS) group decision approach for the multi-criteria evaluation is presented in this study to deal with this problem under uncertainty. A novel multi-criteria group decisionmaking approach considers experts' risk attitudes and views and entropy concepts were developed in the TIFS environment. Furthermore, new ranking scores were proposed through similarity to ideal solutions by the concept of closeness coefficient to prioritize and choose the sustainable indicators. A case study regarding highway construction projects was presented to analyze the sustainable indicators under uncertainty. The considered case study was solved using the introduced group-decision approach.
The primary aim of this paper is to present a sound approach for the assessment and adoption of SIs in highway construction projects. The principal novelties of this study are as follows:

•
To cope with uncertainty in highway construction projects, triangular intuitionistic fuzzy sets (TIFSs) are used. The TIFSs make the process of decision-making more flexible regarding degrees of agreement, disagreement, and hesitancy utilizing a triangular function. • Risk attitudes of experts are considered within the assessment and process of group decision-making because they can have various perspectives, such as optimistic or pessimistic, in their views owing to their various backgrounds and characteristics. • A novel methodology is proposed to specify experts' weights within the process of group decision-making based on the concepts of entropy. • A new compromise ranking score is proposed to evaluate and choose sustainability indicators in highway construction projects.
Ultimately, some sensitivity analyses were performed on the preference order ranking of the top ten SIs in a case study according to the change of approach coefficients and different risk attitudes of experts. The drawn conclusion of the sensitivity analyses was that approach coefficients selected in the case study were suitable choices. Moreover, the presented approach was compared with the traditional fuzzy MCDM techniques, including fuzzy SAW and fuzzy VIKOR. The computational results represented that there was no major difference between the proposed approach and other fuzzy MCDM techniques regarding the priority of SIs in most cases. In addition, both the first and last priorities derived from this approach were the same in all the aforementioned methods.
The introduced comprehensive approach has proposed an efficient decision-making method for highway construction regarding sustainable development principles. In fact, it presented a dependable model in which the results benefited from the merits of taking into account the risk attitudes of experts and the new TIFS-ranking method. Furthermore, the applied fundamental concepts were intelligible to the committee of experts and project managers, and the required calculations were straightforward. Hence, by introducing evaluated sustainable indicators, this paper helps project managers improve highway projects' sustainability and make the most sustainable decisions. As future research, a holistic framework can be developed that utilizes the mentioned criteria and considers environmental and social impacts as criteria in the evaluation of sustainability indicators. In addition, the ranked SIs with higher priority can be used as key indicators in the sustainability assessment of highway construction projects.
Author Contributions: H.H. presented the research methodology, performed the development and experiments of this study; P.G. and F.N. provided extensive advice throughout the study. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.

Conflicts of Interest:
The authors declare no conflict of interest.